5 Beginner Multiple Regression Books to Build Your Foundation

After decades of grappling with complex statistical methods, Jacob Cohen and his co-authors crafted a text that breaks down multiple regression into understandable, applied concepts tailored for behavioral science researchers. You’ll learn to specify regression models that directly tackle your research questions, supported by clear verbal explanations and numerous examples, including chapters that review foundational statistics to strengthen your base. The book’s emphasis on graphics, confidence intervals, and effect size measures enhances your ability to interpret data meaningfully. If you’re a graduate student or researcher in psychology or related fields seeking a less mathematical introduction that still respects statistical rigor, this book offers a grounded, practical pathway.

Drawing from decades of experience in industrial engineering, Douglas C. Montgomery, alongside Elizabeth A. Peck and G. Geoffrey Vining, crafted this book to make regression analysis understandable and applicable. You gain a solid grounding in linear regression concepts, from basic inference to handling complex issues like multicollinearity and model validation, illustrated with examples relevant across science and engineering fields. Chapters also explore robust regression methods and generalized linear models, giving you tools to tackle real data challenges confidently. This book suits those beginning their journey in regression, especially in technical disciplines, offering a rigorous but accessible approach without overwhelming jargon.

This tailored book explores multiple regression through a progressive, beginner-friendly lens designed to build your confidence steadily. It covers foundational concepts at a comfortable pace, removing overwhelm by focusing on your current background and learning goals. The content is carefully tailored to match your interests and skill level, providing clear explanations and practical examples that foster understanding without unnecessary complexity. By guiding you through core ideas step-by-step, it reveals how multiple regression models uncover relationships between variables across diverse fields. This personalized approach ensures you gain thorough knowledge efficiently, helping you navigate the subject with clarity and assurance.

After decades of teaching social science statistics, Aki Roberts and John M. Roberts crafted this book to make multiple regression accessible without requiring more than a basic statistics background. You’ll find clear explanations of how dependent variables relate to multiple independent variables, supported by manageable datasets and practical examples using SPSS, Stata, SAS, and R. The authors’ experience shines through in chapters that demystify complex concepts through straightforward interpretations and hands-on exercises. This book suits advanced undergraduates, beginning graduate students, and anyone looking for a solid refresher before tackling more advanced statistical methods.

During her extensive work teaching statistics, Anusha Illukkumbura noticed many students struggle with the complexities of regression analysis. This book breaks down core concepts like correlation, simple and multiple linear regression, residual tests, and non-linear regression, guiding you through manual calculations and software tools such as Minitab and R. You’ll learn to interpret statistical outputs and understand advanced topics like ANOVA, multi-collinearity, and stepwise regression in a clear, approachable way. It’s a solid starting point if you want to grasp the foundations and nuances of regression without getting lost in jargon or overly technical explanations.

Jacob and Patricia Cohen Cohen’s decades of expertise in behavioral science statistics led to this approachable guide that strips away complex math to focus on practical understanding. You’ll learn how to frame regression models that speak directly to your research questions, with plenty of examples illustrating key concepts like confidence intervals and effect sizes. The book’s structure allows you to tackle chapters independently, making it easier to absorb topics like bivariate correlation or specifying models without feeling overwhelmed. If you’re diving into multiple regression for the first time or seeking a clear, methodical reference, this book offers a grounded introduction tailored to applied research across social sciences.

This tailored book explores the practical application of multiple regression analysis through popular statistical software, designed to match your experience and learning pace. It focuses on guiding you step-by-step from foundational concepts to confident use of code for effective data analysis. By concentrating on your interests and background, it removes overwhelm and builds your skills progressively, making complex topics accessible and engaging. Each chapter reveals clear explanations and practical examples that demystify software functions and coding techniques, helping you unlock the full potential of your data. This personalized approach ensures you engage with content that directly addresses your goals and comfort level, fostering a more rewarding learning journey.